ArticlePloS one2025
Development of label-free cell tracking for discrimination of the heterogeneous mesenchymal migration.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- A novel expert-annotated single-cell dataset for thyroid cancer diagnosis with deep learning benchmarks.PLOS digital health · 2025Article
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3 authors.
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Abstract
Image-based cell phenotyping is fundamental in both cell biology and medicine. As cells are dynamic systems, phenotyping based on static data is complemented by dynamic data extracted from time-dependent cell characteristics. We developed a label-free automatic tracking method for phase contrast images. We examined the possibility of using cell motility-based discrimination to identify different types of mesenchymal migration in invasive malignant cancer and non-cancer cells. These cells were cultured in plastic tissue culture vessels, using motility parameters from cell trajectories extracted with label-free tracking. Correlation analysis with these motility parameters identified characteristic parameters for cancer HT1080 fibrosarcoma and non-cancer 3T3-Swiss fibroblast cell lines. The parameter "sum of turn angles," combined with the "frequency of turns" at shallow angles and "migration speed," proved effective in highlighting the migration characteristics of these cells. It revealed differences in their mechanisms for generating effective propulsive forces. The requirements to characterize these differences included the spatiotemporal resolution of segmentation and tracking, capable of detecting polarity changes associated with cell morphological alterations and cell body displacement. With the segmentation and tracking method proposed here, a discrimination curve computed using quadratic discrimination analysis from the "sum of turn angles" and "frequency of turns below 30°" gave the best performance with a 94% sensitivity. Cell migration is a process related not only to cancer but also to tissue healing and growth. The proposed methodology is easy to use, enabling anyone without professional skills in image analysis, large training datasets, or special devices. It has the potential for application not only in cancer cell discrimination but also in a broad range of applications and basic research. Validating the expandability of this method to characterize cell migration, including the scheme of propulsive force generation, is an important consideration for future study.
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